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Abstract This work proposes and analyzes a structurally-integrated lithium-ion battery concept. The multifunctional energy storage composite (MESC) structures developed here encapsulate lithium-ion battery materials inside high-strength carbon-fiber composites and use interlocking polymer rivets to stabilize the electrode layer stack mechanically. These rivets enable load transfer between battery layers, allowing them to store electrical energy while also contributing to the structural load carrying performance, without any modifications to the battery chemistry. The design rationale, fabrication processes, and experimental mechano-electrical characterization of first-generation MESCs are discussed. Experimental results indicate that the MESCs offer electrochemical performance comparable to standard lithium-ion cells, despite the disruptive design change. The mechanical performance of MESCs is assessed via quasi-static three-point bending tests, with results showing significantly improved mechanical stiffness and strength over traditional pouch cells. The rivets minimize interlayer shear movement of the electrode stack, thus allowing it to maintain electrochemical functionalities while carrying mechanical bending. While minimal load application can cause permanent deformation of pouch cells, MESCs maintain their structural integrity and energy-storage capabilities under realistic repeated loading. The results obtained demonstrate the mechanical robustness of MESCs, which allow them to be fabricated as energy-storing structures for electric vehicles and other applications. Graphical abstract Image 1 Highlights • Multifunctional energy storage composites (MESC) embed battery layers in structures. • Interlocking rivets anchor battery layers which contribute to mechanical performance. • Experimental testing of MESC shows comparable electrochemical behavior to baseline. • At 60% packing efficiency, MESC gain 15× mechanical rigidity compared to pouch cells. • MESC show negligible capacity fading after 1000 bending cycles at 80% design load. [ABSTRACT FROM AUTHOR]

This work presents the feasibility of monitoring state of charge (SoC) and state of health (SoH) of lithium-ion pouch batteries with acousto-ultrasonic guided waves. The guided waves are propagated and sensed using low-profile, built-in piezoelectric disc transducers that can be retrofitted onto off-the-shelf batteries. Both experimental and analytical studies are performed to understand the relationship between guided waves generated in a pitch-catch mode and battery SoC/SoH. The preliminary experiments on representative pouch cells show that the changes in time of flight (ToF) and signal amplitude (SA) resulting from shifts in the guided wave signals correlate strongly with the electrochemical charge-discharge cycling and aging. An analytical acoustic model is developed to simulate the variations in electrode moduli and densities during cycling, which correctly validates the absolute values and range of experimental ToF. It is further illustrated via a statistical study that ToF and SA can be used in a prediction model to accurately estimate SoC/SoH. Additionally, by using multiple sensors in a network configuration on the same battery, a significantly more reliable and accurate SoC/SoH prediction is achieved. The indicative results from this study can be extended to develop a unified guided-wave-based framework for SoC/SoH monitoring of many lithium-ion battery applications. [ABSTRACT FROM AUTHOR]

Accurate interpretation of in-situ piezoelectric sensor signals is a challenging task. This paper presents the development of a numerical compensation model based on physical insight to address the influence of structural loads on piezo-sensor signals. The model requires knowledge of in-situ strain and temperature distribution in a structure while acquiring piezoelectric sensor signals. The parameters of the numerical model are obtained using experiments on flat aluminum plate under uniaxial tensile loading. It is shown that the model parameters obtained experimentally can be used for different structures, and sensor layout. Furthermore, the combined effects of load and temperature on the piezo-sensor response are also investigated and it is observed that both of these factors have a coupled effect on the sensor signals. It is proposed to obtain compensation model parameters under a range of operating temperatures to address this coupling effect. An important outcome of this study is a new load monitoring concept using in-situ piezoelectric sensor signals to track changes in the load paths in a structure. [ABSTRACT FROM AUTHOR]

Ultrasonic guided waves offer a convenient and practical approach to structural health monitoring and non-destructive evaluation. A key property of guided waves is the fully defined relationship between central frequency and propagation characteristics (phase velocity, group velocity and wavenumber)—which is described using dispersion curves. For many guided wave-based strategies, accurate dispersion curve information is invaluable, such as group velocity for localisation. From experimental observations of dispersion curves, a system identification procedure can be used to determine the governing material properties. As well as returning an estimated value, it is useful to determine the distribution of these properties based on measured data. A method of simulating samples from these distributions is to use the iterative Markov-Chain Monte Carlo (MCMC) procedure, which allows for freedom in the shape of the posterior. In this work, a scanning-laser Doppler vibrometer is used to record the propagation of Lamb waves in a unidirectional-glass-fibre composite plate, and dispersion curve data for various propagation angles are extracted. Using these measured dispersion curve data, the MCMC sampling procedure is performed to provide a Bayesian approach to determining the dispersion curve information for an arbitrary plate. The distribution of the material properties at each angle is discussed, including the inferred confidence in the predicted parameters. The percentage errors of the estimated values for the parameters were 10–15 points larger when using the most likely estimates, as opposed to calculating from the posterior distributions, highlighting the advantages of using a probabilistic approach. [ABSTRACT FROM AUTHOR]

New tools for applying force to animals, tissues, and cells are critically needed in order to advance the field of mechanobiology, as few existing tools enable simultaneous imaging of tissue and cell deformation as well as cellular activity in live animals. Here, we introduce a novel microfluidic device that enables high-resolution optical imaging of cellular deformations and activity while applying precise mechanical stimuli to the surface of the worm's cuticle with a pneumatic pressure reservoir. To evaluate device performance, we compared analytical and numerical simulations conducted during the design process to empirical measurements made with fabricated devices. Leveraging the well-characterized touch receptor neurons (TRNs) with an optogenetic calcium indicator as a model mechanoreceptor neuron, we established that individual neurons can be stimulated and that the device can effectively deliver steps as well as more complex stimulus patterns. This microfluidic device is therefore a valuable platform for investigating the mechanobiology of living animals and their mechanosensitive neurons. [ABSTRACT FROM AUTHOR]

Smart structures mimic biological systems by using thousands of sensors serving as a nervous system analog. One approach to give structures this sensing ability is to develop a multifunctional sensor network. Previous work has demonstrated stretchable sensor networks consisting of temperature sensors and impact detectors for monitoring external environments and interacting with other objects. The objective of this work is to develop distributed, robust and reliable strain gauges for obtaining the strain distribution of a designated region on the target structure. Here, we report a stretchable network that has 27 rosette strain gauges, 6 resistive temperature devices and 8 piezoelectric transducers symmetrically distributed over an area of 150 × 150 mm to map and quantify multiple physical stimuli with a spatial resolution of 2.5 × 2.5 mm. We performed computational modeling of the network stretching process to improve measurement accuracy and conducted experimental characterizations of the microfabricated strain gauges to verify their gauge factor and temperature coefficient. Collectively, the results represent a robust and reliable sensing system that is able to generate a distributed strain profile of a common structure. The reported strain gauge network may find a wide range of applications in morphing wings, smart buildings, autonomous cars and intelligent robots. [ABSTRACT FROM AUTHOR]